OASIS TASK 2:UMEMPLOYMENT RATE¶

importing required libraries¶

In [1]:
import numpy as np
import pandas as pd
In [2]:
data=pd.read_csv("Unemp.csv") #reading csv file
In [3]:
data
Out[3]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.740
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.740
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.740
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.740
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.740
... ... ... ... ... ... ... ... ... ...
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855

267 rows × 9 columns

displaying 1st 10 records¶

In [4]:
data.head(10)
Out[4]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.74
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.74
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.74
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.74
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.74
5 Andhra Pradesh 30-06-2020 M 3.31 19805400 47.41 South 15.9129 79.74
6 Andhra Pradesh 31-07-2020 M 8.34 15431615 38.91 South 15.9129 79.74
7 Andhra Pradesh 31-08-2020 M 6.96 15251776 37.83 South 15.9129 79.74
8 Andhra Pradesh 30-09-2020 M 6.40 15220312 37.47 South 15.9129 79.74
9 Andhra Pradesh 31-10-2020 M 6.59 15157557 37.34 South 15.9129 79.74

displaying last 10 records¶

In [5]:
data.tail(10)
Out[5]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
257 West Bengal 31-01-2020 M 6.94 35820789 47.35 East 22.9868 87.855
258 West Bengal 29-02-2020 M 4.92 36964178 47.74 East 22.9868 87.855
259 West Bengal 31-03-2020 M 6.92 35903917 47.27 East 22.9868 87.855
260 West Bengal 30-04-2020 M 17.41 26938836 39.90 East 22.9868 87.855
261 West Bengal 31-05-2020 M 17.41 28356675 41.92 East 22.9868 87.855
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855

data preprocessing¶

In [6]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB
In [7]:
data.describe()
Out[7]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) longitude latitude
count 267.000000 2.670000e+02 267.000000 267.000000 267.000000
mean 12.236929 1.396211e+07 41.681573 22.826048 80.532425
std 10.803283 1.336632e+07 7.845419 6.270731 5.831738
min 0.500000 1.175420e+05 16.770000 10.850500 71.192400
25% 4.845000 2.838930e+06 37.265000 18.112400 76.085600
50% 9.650000 9.732417e+06 40.390000 23.610200 79.019300
75% 16.755000 2.187869e+07 44.055000 27.278400 85.279900
max 75.850000 5.943376e+07 69.690000 33.778200 92.937600
In [8]:
data.shape
Out[8]:
(267, 9)

displaying regions¶

In [9]:
x=data['Region']
x
Out[9]:
0      Andhra Pradesh
1      Andhra Pradesh
2      Andhra Pradesh
3      Andhra Pradesh
4      Andhra Pradesh
            ...      
262       West Bengal
263       West Bengal
264       West Bengal
265       West Bengal
266       West Bengal
Name: Region, Length: 267, dtype: object
In [14]:
y=data[' Estimated Unemployment Rate (%)']
y
Out[14]:
0       5.48
1       5.83
2       5.79
3      20.51
4      17.43
       ...  
262     7.29
263     6.83
264    14.87
265     9.35
266     9.98
Name:  Estimated Unemployment Rate (%), Length: 267, dtype: float64
In [16]:
df2=data.iloc[:,3]
df2
Out[16]:
0       5.48
1       5.83
2       5.79
3      20.51
4      17.43
       ...  
262     7.29
263     6.83
264    14.87
265     9.35
266     9.98
Name:  Estimated Unemployment Rate (%), Length: 267, dtype: float64

importing neccesary libraries¶

In [20]:
import plotly.express as px
import matplotlib.pyplot as plt

visualizations¶

analyzing data by bar graphs¶

In [21]:
fg=px.bar(data,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',title='unemployment rate statewise by bargraph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [22]:
fg=px.bar(data,x='Region.1',y=' Estimated Unemployment Rate (%)',color='Region',title='unemployment rate statewise by bargraph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()

analyzing data by boxplot¶

In [23]:
fg=px.box(data,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',title='unemployment rate statewise by boxplotgraph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()

analyzing data by scatterplot¶

In [24]:
fg=px.scatter(data,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',title='unemployment rate statewise by scattergraph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()

analyzing data by histogram¶

In [25]:
fg=px.histogram(data,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',title='unemployment rate state wise by histogram',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()